Field of the Invention
The present invention relates to information handling systems. More specifically, embodiments of the invention relate to automatic optimization of continuous processes.
Description of the Related Art
As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option available to users is information handling systems. An information handling system generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, information handling systems may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in information handling systems allow for information handling systems to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, information handling systems may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems.
It is known to control continuously operating systems which execute continuous processes using information handling systems. An issue relating to this control is how to optimize underlying parameters driving a data stream in settings across various domains where data about the process are collected continuously. One example of a continuously operating system is a power plant such as a fossil fuel power plants where continuous process data describe the combustion process through multiple measurements such as temperatures and pressures, as well as the actionable parameters controlled by the operators that affect those measurements such as fuel and air flows. Other examples of continuously operating systems include applications of “Internet-of-Things” (IOT) technologies where large numbers of remote sensors collect data continuously to describe some process of interest, such as physiological processes in a patient, or the quality of automated manufacturing processes which are the result of input parameters (to the manufacturing process) also measured continuously (e.g., chemical manufacturing).
In many of these applications it is of interest to identify desirable or optimal states of the continuous process. For example, in a fossil fuel power plant, the optimal mixture of fuel and air to achieve minimal emissions and best heat rates over the entire load (power) range are of interest. In medical applications, the optimal dosages of drugs to achieve stable normal physiological functioning over the normal range of functions would be of interest. There are a number of specific challenges when summarizing and analyzing data (e.g., from measurements) collected to describe continuous processes. For example, measurements are often taken at different time intervals and at different step intervals. It can be difficult to align measurements of parameters for analyses and specifically, for predictive modeling of other process parameters and key process performance indicators measured upstream. Also for example, autocorrelations of parameter measurements can make it challenging to independently change individual process parameters. Continuous processes are often supervised, sometimes through closed-loop systems or via experienced operators, medical professionals, or engineers. However, the parameter values or settings themselves can be the result of specific processing conditions upstream of the process, creating strong autocorrelations in the process data. Also for example, competing complex goal functions can result in outcomes that are not independent of each other, but related to each other in complex ways, and related to upstream parameter settings in complex ways. However, application of known data modeling and predictive analysis methodology assumes clear identification of predictors (e.g., exogenous variables) of the system, and outcomes that depend on the predictors; in continuous processes, this distinction can usually not be made.
It is known to attempt to optimize high-dimensional continuous processes by building of prediction models from the data using some multivariate techniques such as neural nets or partial least squares methods, and then optimizing this process through inverse prediction (i.e., identifying the inputs expected to be associated with optimal performance according to the model). While neural networks based “point” optimization or time-series (time-delayed) neural networks optimization can somewhat successful in some domains, their implementation is often difficult, requiring carefully designed and expensive to maintain closed-loop systems.